What type of regression begins with existing variables and evaluates whether any should be removed in each step?

Stepwise regression is a method that combines both forward selection and backward elimination techniques to evaluate the inclusion or exclusion of variables in a regression model iteratively. This approach begins with either no variables or a specified set of variables and systematically assesses whether any should be added to or removed from the model based on certain criteria, such as statistical significance or the overall fit of the model.

Within stepwise regression, at each step of the process, the method evaluates existing variables and their contribution to the model. If a variable is found to be statistically insignificant when controlling for the others, it can be removed from the model. This iterative process continues until no further improvements can be made, ensuring that only the most relevant predictors remain included.

This method is particularly useful for cases where there are many potential predictors, and it’s not clear which ones are necessary for a robust model. Thus, stepwise regression provides a structured way to refine the model by assessing the necessity of each variable throughout the analysis.